Analog in-memory computing (AIMC) using resistive memory devices has the potential to increase the energy efficiency of deep neural network inference by multiple orders of magnitude. This is enabled by performing matrix vector multiplications – one of the key operations in deep neural network inference – directly within the memory, avoiding expensive weight fetching from external memory such as DRAM. The IBM HERMES Project Chip is a state-of-the-art, 64-core mixed-signal AIMC chip based on Phase Change Memory that makes this concept a reality. Using this chip, we demonstrate automatic deployment and inference of a Transformer model capable of predicting chemical compounds that are formed in a chemical reaction.